MDM RNA-seq Analysis Report

Author

Antoine Chaillon

Published

1 January 2026

Note

RNAseq data from Adam Fields’ Laboratory, University of California San Diego (UCSD).

Summary

  • Number of samples: 180
  • Groups: HIV- moderate cannabis, HIV- daily cannabis, HIV- naive to cannabis, HIV+ naive to cannabis, HIV+ daily cannabis, HIV+ moderate cannabis
  • Treatments: CBD, CBDpIL1B, IL1B, THC, THCpIL1B, Vehicle
  • HIV status: HIVn, HIVp
  • Cannabis use: moderate, daily, naive

Workflow Overview

We processed the RNA-seq data from monocyte-derived macrophages (MDMs) using the following pipeline:

DESeq2 Model

The differential expression model used:

\[ design(dds) ~ treatment + HIV status + cannabis \]

  • treatment: vehicle, IL1b, CBD, THC, CBD+IL1B, THC+IL1b
  • hiv_status: HIV- vs HIV+
  • cannabis: naive, moderate, daily

Summary Analyses

Volcano Plots

volcano_list[["cannabis_daily_vs_naive"]]

PCA Plots

using ntop=500 top features by variance

using ntop=500 top features by variance

using ntop=500 top features by variance

using ntop=500 top features by variance

Top Genes Tables

# DT::datatable(top20_list[["cannabis_daily_vs_naive"]], options = list(pageLength = 5, scrollX = TRUE), rownames = FALSE)

DT::datatable(top20_list[["cannabis_daily_vs_naive"]]|>
  dplyr::mutate(across(where(base::is.numeric), ~ round(.x, 20))),
          rownames = FALSE,
          extensions = 'Buttons',
          options = list(
            dom = 'Bfrtip',
            buttons = c('copy', 'csv', 'excel'),
            pageLength = 10
          ))
Note
  • Only genes with padj < 0.05 and |log2FC| > 1 are labeled in volcano plots.
  • PCA plots can be faceted by hiv_status to observe group separation.
  • Interactive tables allow scrolling and sorting top genes for each contrast.

Pathway enrichment analysis

  • Gene Ontology (GO) enrichment analysis was performed separately for upregulated and downregulated genes for each contrast. Genes with an adjusted p-value < 0.01 and absolute log₂ fold change > 2 were selected. Enrichment analysis was conducted using the clusterProfiler package with the Biological Process (BP) ontology, using all expressed genes as the background universe. Enrichment results were visualized using dot plots, and leading-edge genes contributing to each enriched pathway were extracted for downstream interpretation.

  • Dot plots showing Gene Ontology Biological Process enrichment for upregulated (left) and downregulated (right) genes across experimental contrasts. Genes were considered differentially expressed if they exhibited an adjusted p-value < 0.01 and an absolute log₂ fold change > 2. Dot size represents the number of genes contributing to each pathway, and color indicates the adjusted p-value. Enrichment analyses were performed using the full set of expressed genes as background. Pathways are shown separately for genes increased and decreased in expression to facilitate biological interpretation.

NULL

NULL

Top Differentially Expressed Genes

  • Heatmap showing variance-stabilized expression (VST) of the top N differentially expressed genes for the indicated contrast. Genes were selected based on adjusted p-value and absolute log2 fold change from DESeq2 analysis. Rows represent genes (labeled by gene symbol), and columns represent samples. Expression values are row-scaled (z-score) to emphasize relative expression patterns. Samples are ordered by HIV status, cannabis exposure, and treatment condition.

Selected / Candidate Genes

  • Heatmap showing variance-stabilized expression (VST) of selected genes of interest across samples. Genes were chosen a priori based on biological relevance (e.g., HIV response, inflammation, cannabinoid signaling). Rows represent genes (gene symbols), and columns represent samples. Expression values are row-scaled to highlight relative differences across conditions. Samples are ordered by HIV status, cannabis exposure, and treatment.

Impact of HIV on inflammatory phenotype

Methods

Here we compare PWH vs PWoH within vehicle-treated samples and adjust fo cannabis use.

Differentially Expressed Genes

Volcano plot of DE genes between PWH and PWoH macrophages. TNF is the only significantly upregulated inflammatory gene (padj < 0.05).

Enrichment of inflammatory pathways in vehicle-treated macrophages from PWH

Over-representation analysis (ORA) of genes differentially expressed between PWH and PWoH macrophages under vehicle conditions highlights enrichment of pathways related to cytokine production, adaptive immune responses, and immune effector processes. Point size indicates the number of DE genes in each pathway, while color represents the significance of enrichment (-log10 adjusted p-value)

GSEA of inflammatory pathways in vehicle-treated macrophages.

Ridgeplot from GSEA showing the distribution of pathway genes across the ranked gene list; peaks to the right indicate upregulation in PWH

Dot size indicates pathway size (number of genes). NES = normalized enrichment score; positive values indicate upregulation in PWH. Red dots indicate pathways where TNF is part of the core enrichment contributing to the NES. All three pathways are significantly enriched (NES 2.17–2.23, p.adjust < 0.01).

Modulation of IL1-B response by Cannabis Use

Important

IL1B induces a strong inflammatory response in PWH naive to cannabis, with upregulation of classic cytokine and immune effector genes (e.g., IL1B, IL6, TNFAIP6). Moderate and daily cannabis use appears to dampen this response, as most genes are not significantly induced in these groups. This suggests cannabis exposure may modulate macrophage inflammatory activation.

Method

Model rationale

The interaction framework allows us to distinguish:

  • Main effect of cannabis: baseline differences in gene expression between cannabis exposure groups
  • Main effect of treatment: transcriptional changes induced by IL1B stimulation
  • Interaction effect: whether the effect of IL1B stimulation differs depending on cannabis exposure. The interaction term specifically tests whether the IL1B-induced transcriptional response is modified by cannabis use, rather than assuming identical treatment effects across exposure groups.

Statistical model

For each gene (i), counts were modeled using a negative binomial distribution:

\[ log(μi​)=β0​+β1​⋅Cannabis+β2​⋅Treatment+β3​⋅(Cannabis×Treatment) \]

Where:

  • μi is the expected expression of gene (i)
  • β0 is the intercept
  • β1 represents the main effect of cannabis exposure
  • β2 represents the main effect of IL1B treatment
  • β3 represents the interaction effect, capturing differential IL1B responses by cannabis status

Approach

RNA-seq data from MDM of PWH are analyzed using DESeq2. Genes responsive to IL1B stimulation are filtered to retain only those significant in at least one cannabis use condition (adjusted p < 0.05). Log2 fold-change (log2FC) values are extracted for naive, moderate, and daily cannabid exposure. Heatmaps were generated using ComplexHeatmap, with non-significant log2FC values desaturated and significant changes indicated with an asterisk (*). Key inflammatory genes (e.g., IL1B, TNF, CXCL10) were highlighted in bold. Interactive tables were produced with DT, combining log2FC values and significance for each condition.

Results

Heatmap showing log2 fold-change (log2FC) of IL1B-responsive inflammatory genes across three cannabis conditions: naive , moderate, and daily. Only genes significant in at least one condition (adjusted p < 0.05) are included. Log2FC values for non-significant conditions are shown but visually desaturated, while significant changes are highlighted with an asterisk (*). Blue, white, and red indicate negative, no change, and positive log2FC, respectively. Gray squares represent missing values (NA). Rows are clustered based on expression patterns. The log2FC color legend is shown on the right, and significance is indicated in a separate legend.

IL1B-induced inflammatory response modulated by CBD or THC

Here we compared inflammatory response triggered by IL-1B alone, IL-1B + THC and IL-B+CBD and adjusted for HIV status in persons Cannabis Naive.

1:1 Comparison IL-1B ± THC or CBD versus Vehicle

1:1 Comparison IL-1B modulation by THC or CBD

Cannabis * HIV Status Interaction models.

In HIV+ vs HIV- individuals

DESeq2 Model

The differential expression model used:

\[ design(dds_vehicle) <- ~ cannabis * hiv_status \]

Results

Important

Under vehicle conditions, TNF expression in macrophages from PWH was higher than in PWoH at the canonical transcript level (log₂FC ≈ +2.9), consistent with modest basal inflammatory activation. However, this difference did not reach statistical significance after multiple testing correction (FDR ≈ 1.0).

In HIV+ individuals, does moderate cannabis reduce TNF compared to naive?

DESeq2 Model

The differential expression model used:

\[ design(dds Vehicle HIVp) <- ~ cannabis \]

HIV+ moderate vs naive

HIV+ daily vs naive

HIV+ daily+moderate vs naive

Important

In HIV-positive macrophages under vehicle conditions, cannabis use was associated with a dose-dependent reduction in TNF expression, with moderate and daily users showing markedly lower TNF levels compared to cannabis-naive individuals (log₂FC −3.6 and −4.1, respectively; nominal p < 0.05).

TNF summary

Canonical TNF Transcripts

TNF expression in macrophages by HIV status and cannabis use.

Note

Normalized RNA-seq counts for all TNF transcripts (all ENSEMBL IDs annotated as TNF) were summed per sample and plotted as mean ± 95% confidence interval. PWH = people with HIV; PWoH = people without HIV. Bars show TNF expression under vehicle conditions stratified by cannabis use (naive, moderate, daily).

CFAR Logo